1. Technical Field
The present invention relates to medical image analysis, and more particularly, to a system and method for three-dimensional (3D) visualization of lung perfusion or density and a method for analyzing lung perfusion or density distribution in patients for diagnosis.
2. Discussion of the Related Art
A pulmonary embolism occurs when a piece of a blood clot from a deep vein thrombosis (DVT) breaks off and travels to an artery in a lung where it blocks the artery, damages the lung and puts a strain on the heart. This short-term complication is potentially life threatening and occurs in about ten percent of patients with acute DVT events. It may be even more common than generally realized because a majority of embolisms occur without symptoms.
The majority of people recover fully from a DVT and pulmonary embolism. However, a large pulmonary embolism can block almost all of the blood flow to a portion of the lungs and cause sudden death. In addition, a pulmonary embolism can put a severe strain on the heart. After ischemic heart disease and stroke, a pulmonary embolism is the third leading cause of death from heart disease. Yet it may be the most common preventable cause of death in hospitals.
Given the nature of pulmonary embolism, timely diagnosis is critical. However, it is also important to assess how emboli affect blood flow in the lungs. Recently, there has been a growing research interest in automatic methods for detection of pulmonary emboli from high-resolution computed tomography angiography (CTA). In addition, there has been an interest in methods for visualizing and assessing the extent and location of perfusion deficits caused by a pulmonary embolism. Such techniques utilize multi-slice computed tomography (CT) machines that routinely generate 600 or more two-dimensional (2D) slices per patient to identify segmental and sub-segmental emboli. However, this can be time-consuming and does not lend itself to immediate visualization of lung perfusion.
Recently, however, researchers have presented an experimental method for 2D visualization of lung perfusion within the parenchyma following administration of intravenous contrast. This method produces a 2D visualization of color-coded parenchymal perfusion overlaid on an original CT image. While this information does convey some useful information, it is difficult to keep track of regions of abnormal perfusion. Further, if there is a pulmonary embolus proximal to such a region, keeping track of where it is located with respect to the region is arduous.
Accordingly, there is a need for a technique of viewing a 3D map for highlighting areas of diminished or abnormal perfusion or abnormal regions within the parenchyma thereby enabling the identification of pulmonary emboli or other abnormalities and a technique for analyzing such data for diagnosis.